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Retain what matters
Save reusable knowledge while skipping temporary, duplicate, or low-value details.
Predictive memory infrastructure for AI
AI learns continuously.
Its memory should too.
MemorySafe gives AI a decision layer for what to retain, protect, replay, and forget—before useful knowledge disappears or low-value memory takes over.
Agent private beta · coming soonContinual-learning pilots · conversations open
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Keep control visible
Review, protect, and delete memories instead of treating storage as a black box.
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Govern within limits
Estimate vulnerability, respect bounded memory, and record why each decision was made.
How the product is organized · two paths
Built for AI that needs continuity.
One path governs what an AI assistant remembers about people and work. The other governs replay memory while a model continues learning.
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Upcoming private betaMemorySafe for AI Agents
A local governed-memory layer designed to keep useful context reusable, make stored memories visible, and let the user review or delete them.
Join the beta list →CL
Pilot partnershipsMemorySafe for Continual Learning
A memory-governance policy designed to balance value, vulnerability, redundancy, and capacity when models learn from changing data.
Discuss a pilot →How MemorySafe works
Make memory a decision.
MemorySafe evaluates incoming memory before limited capacity is allocated, then makes the action and reason inspectable.
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AssessConsider value, estimated vulnerability, redundancy, and available capacity.
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Apply policyDecide what should be retained, protected, replayed, merged, or forgotten.
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Record the reasonKeep governance actions visible and auditable instead of silently changing memory.
52-second founder overviewEnglish audio · embedded captions
Evidence with boundaries
Measured carefully.
Claimed carefully.
The Agent path is a product preview. The continual-learning figures are an internal paired benchmark, not peer reviewed and not a promise of deployment performance.
0.702combined AUPRC · MemorySafe
0.763rare-class recall · best of six
12.9 sper seed · matched reservoir
20paired held-out seeds · 52–71
Governance controls are visible.
The sanitized dashboard shows automatic selection, protected memories, duplicates avoided, local storage, and review/deletion controls. The private beta is not released yet.
Best observed of six at the same buffer.
With identical 500-sample buffers, MemorySafe reached 0.702 ±0.042 AUPRC versus 0.687 for reservoir. The paired difference was not significant (p = 0.15).
View methodology and claim boundaries
PneumoniaMNIST was evaluated as a five-task class-incremental stream. All six policies used the same 500-sample buffer, backbone, schedule, task order, and paired seeds 52–71; only sample selection varied. The 95% confidence interval for the AUPRC difference versus reservoir was −0.004 to +0.033, and only the margin over MIR survived Holm correction (p = 0.024). Forgetting was limited in this setup. This internal benchmark is not peer reviewed and does not guarantee deployment performance or savings. Real-world pilot validation is next.
Program memberships
Startup-support program participation; not customer endorsements or validation partners.
awsACTIVATE MEMBER
Priority pilot domains
Where forgetting
has consequences.
These are target domains for continual-learning pilots—not claims of current deployment.
Medical AI
Rare clinical cases can be overwritten as models absorb new data.
Prioritize vulnerable, clinically important samples.
Fraud Detection
Emerging fraud patterns are rare before they become obvious.
Retain valuable anomalies as behaviour changes.
Edge AI
Strict storage and compute limits make every retained sample matter.
Allocate bounded memory intentionally.
Robotics
New environments can interfere with previously learned skills.
Protect fragile capabilities during adaptation.Benefit & ROI simulator
What could MemorySafe change for your system?
Choose a product path and adjust the visible planning assumptions. The outputs are illustrative scenarios, not forecasts.
Simple team example · change any number
Preferences, project details, or recurring instructions.
50% means 10 minutes of repetition becomes about 5. It is a planning scenario—not a measured result.
Illustrative annual scenarioNot a forecast
Current repetition time / year—
Time potentially returned / year—
Illustrative annual value—
Want to calculate ROI too?
Pricing is not final. ROI appears only when you provide your own estimate.
ROI using your estimateIllustrative value minus estimated cost
Net annual value—
Illustrative ROI—
Simple payback—
How this works
Team time today = people × weekly minutes × 52. Potential time returned applies the selected scenario. Illustrative value multiplies those returned hours by the hourly value.
Illustrative scenario only—not a promise of savings. Excludes setup time, model costs, and changes in answer quality.
Technical planning example · keep any value you do not know
This changes only the use-case wording—not the calculation.
A replay sample is one saved training example reviewed again later; 500 is an example.
Use dataset or storage information; 256 KB is an example.
Check training or deployment logs; 200 is an example.
At 30%, 500 becomes about 350. This is not a recommended or proven reduction; the benchmark kept every policy at 500.
Illustrative annual replay scenarioNot a forecast
Modeled replay data not processed / year—
Modeled replay runtime avoided / year—
Illustrative gross annual value—
ROI using your estimateGross value minus estimated investment
Net annual value—
Illustrative ROI—
Simple payback—
How this scenario is calculated
Samples not replayed = current buffer − modeled buffer. Annual data applies sample size, runs per day, and 365 days. Runtime assumes duration falls in direct proportion to buffer reduction.
Planning example only—not a forecast or a recommended buffer size. The benchmark did not test reduction, deployment savings, or whether model quality would be preserved with fewer samples.
Local control
Your memory policy stays inspectable.
The governed-memory database is stored locally. MemorySafe is designed to make stored knowledge and governance actions reviewable rather than invisible.
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Local database
Governed memory records are stored on the user’s device.
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User controls
Review, protect, and delete memories from the product interface.
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Auditable reasons
Retain and evict actions can carry a visible reason.
Connected AI services may process selected context under their own terms. “Local” describes the MemorySafe governed-memory database, not every connected service.
From neuroscience to machine learning
MemorySafe began with a neuroscience question.
Human memory does not preserve everything equally. Why should artificial memory?
I used to study human memory in clinical research. Now I design memory systems for AI.
At MemorySafe Labs, we are building infrastructure for continual learning—inspired by the brain, grounded in science, and designed for real-world impact.
Leadership & advisory
Built with operational and startup experience.
Financial discipline, company-building strategy, technical perspective, and commercial execution.
Head of Operations
Larissa Centeno
Finance leader bringing cost discipline, risk governance, and structured growth strategy.
LinkedIn →
Strategic Advisor
Ankit Mishra, MBA
Startup operator, AI strategist, and venture-capital professional with 13+ years of experience.
LinkedIn →
Sales Director
Grégoire Gervais-Vachon
Bilingual commercial leader connecting financial-services needs with governed AI-memory solutions.
LinkedIn →Choose your path
Give AI a policy for what it remembers.
Join the upcoming Agent beta or explore a scoped continual-learning pilot with clear evidence boundaries.